Developing a generic rRNA capture system for transcriptome studies (microarray and RNAseq)
|has title::Developing a generic rRNA capture system for transcriptome studies (microarray and RNAseq)|
|Student name:||student name::Sander Tan|
|Second reader:||has second reader::Sanne Abeln|
Introduction: Messenger RNA (mRNA) amplification is often used for transcriptome analysis, due to the poly-A tail of mRNA in eukaryotes. Prior to this, the total RNA is purified, which only consists of 1-5% of the to be measured mRNA Although it is known that other RNA (which consists mainly of ribosomal RNA (rRNA)) can interfere the amplification reactions, it is rarely taken into account. It would be desirable to remove the rRNA in a robust way from the reactions. Recent literature from 2010 (Stewart et al., The ISME Journal (2010) 4, 896-907 & Shaomei He et al., Nature Methods (2010) 10, 807-812) shows several methods to get this done, with mixed results. To build a successful generic rRNA capturing system, it is necessary to create specific software and develop a laboratory methodology.
Bioinformatics part 1 (dry lab): At the beginning of the project, a database shall be constructed containing every public available ribosomal sequence. Based on this information, variable and constant regions can be defined. Additionally, it will be defined for which species these regions are constant. For these constant regions, rRNA probes will be designed for the rRNA capture system.
To do this, an inventory of available sequence information has to be made, based on the SILVA rRNA project (http://www.arb-silva.de/). Software will be developed and tested to allow for sequence analysis and probe design. The different stages of process are: 1. Database design 2. Filling the database 3. Creation of “premade queries” 4. Building of a front end (integration of storage, alignment tools and probe design tools)
Molecular part (wet lab): The developed methodology will be based on Danio rerio (zebra fish), so that a proof of concept can be provided. Several oligonucleotides will be designed against the 28S rRNA. Oligonucleotides with a biotin label will be used in combination with MagBeads to remove the 28S rRNA. Different conditions will be tested to optimise this process, such as total RNA concentration, oligonucleotide concentration, temperature hybridization etc. The ratio of 28S rRNA relative to 18S rRNA will be determined by capillary gel electrophoresis (BioAnalyzer). When sufficient 28S rRNA is removed, qPCR shall be used to look at the different ratios.
Should this approach fail prematurely, then the backup plan is to amplify the rRNA sequence from genomic material. The rRNA sequence will be amplified linear using a T7-promoter and used as a capture-probe for the 28S rRNA. The added value of the capturing procedure will be validated by microarray analyse.
Bioinformatics part 2 (dry lab): The microarray data will be analysed with R. The R scripting language and statistics have to be learned (such as expectation maximization, Bayesian analyse). We expect to find more transcripts after capturing. To validate this hypothesis, it is necessary to test a present/absent methodology, which is designed for genome analyse (CGH), for transcriptome analyse.